Android Malware Category and Family Identification Using Parallel Machine Learning

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El Fiky Ahmed Hashem; Madkour Mohamed Ashraf; Shenawy Ayman El

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Journal Paper

Paper Information

Journal: Journal of Information Technology Management Year:2022 | Volume:14 | Issue:4 Page(s): 19-39

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Title

Android Malware Category and Family Identification Using Parallel Machine Learning

Author(s)

El Fiky Ahmed Hashem | Madkour Mohamed Ashraf | Shenawy Ayman El | Issue Writer Certificate

Keywords

Android Malware

Malware Analysis

Malware Category Classification

Malware Family Classification

Malware Dynamic Analysis

Abstract

Android Malware is one of the most dangerous threats on the Internet. It has been on the rise for several years. As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc. One of the most growing attacks is on Android systems due to its use in many devices worldwide. De-spite significant efforts in detecting and classifying Android Malware, there is still a long way to improve the detection process and the classification performance. There is a necessity to provide a basic understanding of the behavior displayed by the most common Android Malware categories and families. Hence, understand the distinct ob-jective of malware after identifying their family and category. This paper proposes an effective systematic and functional parallel machine-learning model for the dynamic detection of Android Malware categories and families. Standard machine learning classifiers are implemented to analyze a massive malware dataset with 14 major mal-ware categories and 180 prominent malware families of the CCCS-CIC-AndMal2020 on dynamic layers to detect Android Malware categories and families. The paper ex-periments with many machine learning algorithms and compares the proposed model with the most recent related work. The results indicate more than 96 % accuracy for Android Malware Category detection and more than 99% for Android Malware family detection overperforming the current related methods. The proposed model offers a highly accurate method for dynamic analysis of Android Malware that cuts down the time required to analyze smartphone malware.

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